Strategies in activating lymphatic system on symptom distress and health-related quality of life in patients with heart failure: secondary analysis of a pilot randomized controlled trial.

Background: Abnormal interstitial fluid accumulation remains the major cause for patients with heart failure (HF) to endure a myriad of distressing symptoms and a decline in their health-related quality of life (HRQoL). The lymphatic system is essential in regulating fluid balance within the interstitial compartment and has recently been recognized as an important target for the prevention and mitigation of congestion. This study aimed to investigate the effects of exercises in activating lymphatic system on symptom distress and HRQoL among patients with HF. Methods and results: This was a pre-determined, secondary analysis of the TOLF-HF [The-Optimal-Lymph-Flow for Heart Failure (TOLF-HF)] study, a two-arm pilot randomized controlled trial evaluating the preliminary effects of the lymphatic exercise intervention in enhancing interstitial decongestion among patients with HF. Participants were randomized to receive either a four-week TOLF-HF program in addition to standard care or standard care alone. The Chinese version of the Minnesota Living with Heart Failure Questionnaire (MLHFQ) was employed to measure symptom distress and HRQoL before and after the intervention. Data analyses included descriptive statistics, the independent sample t-test, Pearson's chi-square test, the Mann-Whitney U test, and covariance analysis. Of the 66 patients enrolled, 60 completed the study. The study results exhibited that the TOLF-HF intervention were effective in alleviating both physical and psychological symptom distress. The intervention group yielded significantly lower MLHFQ total scores in comparison to the control group. The odd ratio of achieving meaningful improvement in HRQoL in TOLF-HF group was 2.157 times higher than those in the control group. Conclusions: The TOLF-HF program focusing on activating lymphatic system was effective in alleviating physical and psychological symptom distress as well as improving HRQoL for patients with HF. The tolerability, feasibility, and effectiveness of the TOLF-HF intervention make it a promising intervention for patients to manage HF. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx, identifier (ChiCTR2000039121).

Exercises in activating lymphatic system on fluid overload symptoms, abnormal weight gains, and physical functions among patients with heart failure: A randomized controlled trial.

Background: Fluid overload remains a vexing problem in management of heart failure. The lymphatic system that plays the central role in fluid homeostasis has recently been explored as a potential target to counteract tissue fluid overload. The goal of the study was to evaluate the preliminary effects of exercises in activating lymphatic system on fluid overload symptoms, abnormal weight gains, and physical functions for patients with heart failure. Methods and results: A pilot, pre- and post-test, randomized clinical trial was conducted to recruit a total of 66 patients who were randomized to receive either a 4-week The-Optimal-Lymph-Flow for Heart Failure (TOLF-HF) program or usual care alone. The primary outcome was the prevalence and burden of the fluid overload symptoms. Findings of the trial showed that the TOLF-HF intervention was effective in reducing the prevalence or burden of the majority of fluid overload symptoms. TOLF-HF intervention also demonstrated significant improvement in the outcomes of abnormal weight gains (MD: -0.82; 95% CI: -1.43 to -0.21; P = 0.010) and physical functions (F = 13.792, P < 0.001). Conclusions: The TOLF-HF program focusing on activating lymphatic system through the performance of therapeutic lymphatic exercises holds the promise as an adjuvant therapy for patients with heart failure to manage fluid overload symptoms, reduce abnormal weight gains, and improve physical functions. Future larger-scale study with longer duration of follow-up is needed. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx, identifier ChiCTR2000039121.

Municipal Ethnic Composition and Disparities in COVID-19 Infections in New Jersey: A Blinder-Oaxaca Decomposition Analysis.

COVID-19 has disproportionally impacted Latinx and Black communities in the US. Our study aimed to extend the understanding of ethnic disparities in COVID-19 case rates by using a unique dataset of municipal case rates across New Jersey (NJ) during the first 17 months of the pandemic. We examined the extent to which there were municipal-level ethnic disparities in COVID-19 infection rates during three distinct spikes in case rates over this period. Furthermore, we used the Blinder-Oaxaca decomposition analysis to identify municipal-level exposure and vulnerability factors that contributed to ethnic disparities and how the contributions of these factors changed across the three initial waves of infection. Two clear results emerged. First, in NJ, the COVID-19 infection risk disproportionally affected Latinx communities across all three waves during the first 17 months of the pandemic. Second, the exposure and vulnerability factors that most strongly contributed to higher rates of infection in Latinx and Black communities changed over time as the virus, alongside medical and societal responses to it, also changed. These findings suggest that understanding and addressing ethnicity-based COVID-19 disparities will require sustained attention to the systemic and structural factors that disproportionately place historically marginalized ethnic communities at greater risk of contracting COVID-19.

New Jersey COVID-19 municipal dataset.

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.

Working memory and spatial judgments: Cognitive load increases the central tendency bias.

Previous work demonstrates that memory for simple stimuli can be biased by information about the distribution of which the stimulus is a member. Specifically, people underestimate values greater than the distribution's average and overestimate values smaller than the average. This is referred to as the central tendency bias. This bias has been explained as an optimal use of both noisy sensory information and category information. In largely separate literature, cognitive load (CL) experiments attempt to manipulate the available working memory of participants in order to observe the effect on choice or judgments. In two experiments, we demonstrate that participants under high cognitive load exhibit a stronger central tendency bias than when under a low cognitive load. Although not anticipated at the outset, we also find that judgments exhibit an anchoring bias not described previously.

Perception-memory interactions reveal a computational strategy for perceptual constancy.

A key challenge for the visual system is to extract constant object properties from incoming sensory information. This information is ambiguous because the same sensory signal can arise from many combinations of object properties and viewing conditions and noisy because of the variability in sensory encoding. The competing accounts for perceptual constancy of surface lightness fall into two classes of model: One derives lightness estimates from border contrasts, and another explicitly infers surface reflectance. To test these accounts, we combined a novel psychophysical task with probabilistic implementations of both models. Observers compared the lightness of two stimuli under a memory demand (a delay between the stimuli), a context change (different surround luminance), or both. Memory biased perceived lightness toward the mean of the whole stimulus ensemble. Context change caused the classical simultaneous lightness contrast effect, in which a target appears lighter against a dark surround and darker against a light surround. These effects were not independent: Combined memory load and context change elicited a bias smaller than predicted assuming an independent combination of biases. Both models explain the memory bias as an effect of prior expectations on perception. Both models also produce a context effect, but only the reflectance model correctly describes the magnitude. The reflectance model, finally, captures the memory-context interaction better than the contrast model, both qualitatively and quantitatively. We conclude that (a) lightness perception is more consistent with reflectance inference than contrast coding and (b) adding a memory demand to a perceptual task both renders it more ecologically valid and helps adjudicate between competing models.

Why some colors appear more memorable than others: A model combining categories and particulars in color working memory.

Categorization with basic color terms is an intuitive and universal aspect of color perception. Yet research on visual working memory capacity has largely assumed that only continuous estimates within color space are relevant to memory. As a result, the influence of color categories on working memory remains unknown. We propose a dual content model of color representation in which color matches to objects that are either present (perception) or absent (memory) integrate category representations along with estimates of specific values on a continuous scale ("particulars"). We develop and test the model through 4 experiments. In a first experiment pair, participants reproduce a color target, both with and without a delay, using a recently influential estimation paradigm. In a second experiment pair, we use standard methods in color perception to identify boundary and focal colors in the stimulus set. The main results are that responses drawn from working memory are significantly biased away from category boundaries and toward category centers. Importantly, the same pattern of results is present without a memory delay. The proposed dual content model parsimoniously explains these results, and it should replace prevailing single content models in studies of visual working memory. More broadly, the model and the results demonstrate how the main consequence of visual working memory maintenance is the amplification of category related biases and stimulus-specific variability that originate in perception.

The effect of memory and context changes on color matches to real objects.

Real-world color identification tasks often require matching the color of objects between contexts and after a temporal delay, thus placing demands on both perceptual and memory processes. Although the mechanisms of matching colors between different contexts have been widely studied under the rubric of color constancy, little research has investigated the role of long-term memory in such tasks or how memory interacts with color constancy. To investigate this relationship, observers made color matches to real study objects that spanned color space, and we independently manipulated the illumination impinging on the objects, the surfaces in which objects were embedded, and the delay between seeing the study object and selecting its color match. Adding a 10-min delay increased both the bias and variability of color matches compared to a baseline condition. These memory errors were well accounted for by modeling memory as a noisy but unbiased version of perception constrained by the matching methods. Surprisingly, we did not observe significant increases in errors when illumination and surround changes were added to the 10-minute delay, although the context changes alone did elicit significant errors.

The central tendency bias in color perception: effects of internal and external noise.

Perceptual estimates can be biased by previously seen stimuli in delayed estimation tasks. These biases are often toward the mean of the whole stimulus set. Recently, we demonstrated such a central tendency bias in delayed color estimation. In the Bayesian framework of perceptual inference, perceptual biases arise when noisy sensory measurements are combined with prior information about the world. Here, we investigate this idea in color perception by manipulating stimulus range and stimulus noise while characterizing delayed color estimates. First, we manipulated the experimental prior for stimulus color by embedding stimuli in collections with different hue ranges. Stimulus range affected hue bias: Hue estimates were always biased toward the mean of the current set. Next, we studied the effect of internal and external noise on the amount of hue bias. Internal noise was manipulated by increasing the delay between the reference and test from 0.4 to 4 s. External noise was manipulated by increasing the amount of chromatic noise in the reference stimulus, while keeping the delay between the reference and test constant at 2 s. Both noise manipulations had a reliable effect on the strength of the central tendency bias. Furthermore, there was a tendency for a positive relationship between variability of the estimates and bias in both noise conditions. In conclusion, observers are able to learn an experimental hue prior, and the weight on the prior can be manipulated by introducing noise in the estimation process.

Relating color working memory and color perception.

Color is the most frequently studied feature in visual working memory (VWM). Oddly, much of this work de-emphasizes perception, instead making simplifying assumptions about the inputs served to memory. We question these assumptions in light of perception research, and we identify important points of contact between perception and working memory in the case of color. Better characterization of its perceptual inputs will be crucial for elucidating the structure and function of VWM.

Stimulus-specific variability in color working memory with delayed estimation.

Working memory for color has been the central focus in an ongoing debate concerning the structure and limits of visual working memory. Within this area, the delayed estimation task has played a key role. An implicit assumption in color working memory research generally, and delayed estimation in particular, is that the fidelity of memory does not depend on color value (and, relatedly, that experimental colors have been sampled homogeneously with respect to discriminability). This assumption is reflected in the common practice of collapsing across trials with different target colors when estimating memory precision and other model parameters. Here we investigated whether or not this assumption is secure. To do so, we conducted delayed estimation experiments following standard practice with a memory load of one. We discovered that different target colors evoked response distributions that differed widely in dispersion and that these stimulus-specific response properties were correlated across observers. Subsequent experiments demonstrated that stimulus-specific responses persist under higher memory loads and that at least part of the specificity arises in perception and is eventually propagated to working memory. Posthoc stimulus measurement revealed that rendered stimuli differed from nominal stimuli in both chromaticity and luminance. We discuss the implications of these deviations for both our results and those from other working memory studies.

Short-term memory affects color perception in context.

Color-based object selection - for instance, looking for ripe tomatoes in the market - places demands on both perceptual and memory processes: it is necessary to form a stable perceptual estimate of surface color from a variable visual signal, as well as to retain multiple perceptual estimates in memory while comparing objects. Nevertheless, perceptual and memory processes in the color domain are generally studied in separate research programs with the assumption that they are independent. Here, we demonstrate a strong failure of independence between color perception and memory: the effect of context on color appearance is substantially weakened by a short retention interval between a reference and test stimulus. This somewhat counterintuitive result is consistent with Bayesian estimation: as the precision of the representation of the reference surface and its context decays in memory, prior information gains more weight, causing the retained percepts to be drawn toward prior information about surface and context color. This interaction implies that to fully understand information processing in real-world color tasks, perception and memory need to be considered jointly.

The effect of background and illumination on color identification of real, 3D objects.

For the surface reflectance of an object to be a useful cue to object identity, judgments of its color should remain stable across changes in the object's environment. In 2D scenes, there is general consensus that color judgments are much more stable across illumination changes than background changes. Here we investigate whether these findings generalize to real 3D objects. Observers made color matches to cubes as we independently varied both the illumination impinging on the cube and the 3D background of the cube. As in 2D scenes, we found relatively high but imperfect stability of color judgments under an illuminant shift. In contrast to 2D scenes, we found that background had little effect on average color judgments. In addition, variability of color judgments was increased by an illuminant shift and decreased by embedding the cube within a background. Taken together, these results suggest that in real 3D scenes with ample cues to object segregation, the addition of a background may improve stability of color identification.

A Bayesian model of lightness perception that incorporates spatial variation in the illumination.

The lightness of a test stimulus depends in a complex manner on the context in which it is viewed. To predict lightness, it is necessary to leverage measurements of a feasible number of contextual configurations into predictions for a wider range of configurations. Here we pursue this goal, using the idea that lightness results from the visual system's attempt to provide stable information about object surface reflectance. We develop a Bayesian algorithm that estimates both illumination and reflectance from image luminance, and link perceived lightness to the algorithm's estimates of surface reflectance. The algorithm resolves ambiguity in the image through the application of priors that specify what illumination and surface reflectances are likely to occur in viewed scenes. The prior distributions were chosen to allow spatial variation in both illumination and surface reflectance. To evaluate our model, we compared its predictions to a data set of judgments of perceived lightness of test patches embedded in achromatic checkerboards (Allred, Radonjic, Gilchrist, & Brainard, 2012). The checkerboard stimuli incorporated the large variation in luminance that is a pervasive feature of natural scenes. In addition, the luminance profile of the checks both near to and remote from the central test patches was systematically manipulated. The manipulations provided a simplified version of spatial variation in illumination. The model can account for effects of overall changes in image luminance and the dependence of such changes on spatial location as well as some but not all of the more detailed features of the data.

Lightness perception in high dynamic range images: local and remote luminance effects.

We measured the perceived lightness of target patches embedded in high dynamic range checkerboards. We independently varied the luminance of checks immediately surrounding the test and those remote from it. The data establish context transfer functions (CTFs) that characterize perceptual matches across checkerboard contexts. Several features of the CTFs are broadly consistent with previous research: Matched luminance decreases when overall context luminance decreases; matched luminance increases when overall context luminance increases; manipulating context locations near the target has a greater effect than manipulating locations far from the target patch. The measured CTFs are not well described, however, by changes with context in multiplicative gain alone or by changes in both multiplicative and subtractive adaptation parameters. We were able to fit the data with a three-parameter model of adaptation. This allowed us to characterize the CTFs by specifying the luminances that appeared white, black, and gray (white point, black point, and gray point, respectively). The white and black points depended additively on the local and remote contrasts, but accounting for the gray point required an interaction term. Analysis of this effect suggests that the target patch itself must be included in a description of the visual context.

The dynamic range of human lightness perception.

Natural viewing challenges the visual system with images that have a dynamic range of light intensity (luminance) that can approach 1,000,000:1 and that often exceeds 10,000:1 [1, 2]. The range of perceived surface reflectance (lightness), however, can be well approximated by the Munsell matte neutral scale (N 2.0/ to N 9.5/), consisting of surfaces whose reflectance varies by about 30:1. Thus, the visual system must map a large range of surface luminance onto a much smaller range of surface lightness. We measured this mapping in images with a dynamic range close to that of natural images. We studied simple images that lacked segmentation cues that would indicate multiple regions of illumination. We found a remarkable degree of compression: at a single image location, a stimulus luminance range of 5,905:1 can be mapped onto an extended lightness scale that has a reflectance range of 100:1. We characterized how the luminance-to-lightness mapping changes with stimulus context. Our data rule out theories that predict perceived lightness from luminance ratios or Weber contrast. A mechanistic model connects our data to theories of adaptation and provides insight about how the underlying visual response varies with context.

Contrast, constancy, and measurements of perceived lightness under parametric manipulation of surface slant and surface reflectance.

Across many scenes, local contrast provides a valid cue to surface reflectance, but it is not the only such cue. To generalize beyond theories of lightness that rely exclusively on local contrast, we need to know which other potential cues matter. We had observers make lightness matches between two scene locations, and varied the surface slant and local surround reflectance of one of the locations. When local contrast was a valid cue to reflectance, all observers were approximately lightness constant. When it was not, observers' lightness matches were intermediate between contrast matching and lightness constancy. For most observers, surface slant exerted an effect on perceived lightness beyond that explainable by local contrast.

Quantitative comparison between neural response in macaque inferotemporal cortex and behavioral discrimination of photographic images.

Inferotemporal (IT) cortex plays a critical role in the primate ability to perceive and discriminate between images, but the relationship between responses of single neurons and behavioral capacities is poorly understood. We studied this relationship by recording from IT neurons while monkeys performed a delayed-match-to-sample task with two images. On each day, two sample images were chosen to maximize the selectivity of the neuron and task difficulty was manipulated by varying sample duration and by masking the sample. On each trial, monkeys reported which of the two sample images was presented. Neural performance was described using an ideal-observer analysis. Across the population, neural and behavioral sensitivity to changes in sample duration were indistinguishable. Neural sensitivity was dependent on epoch used to analyze neural response; maximal neural sensitivity was achieved in the 128-ms epoch that began 85 ms after sample onset. At most sample durations, the epoch that yielded optimal neural performance was longer than the sample duration, suggesting that neural selectivity persisted after the presentation of the mask during performance of the task. A control experiment showed that neural and behavioral performance improved in the absence of the mask. These observations suggest that the responses of individual IT neurons contain sufficient information to allow behavioral discrimination of images in a demanding task.